scholarly journals An automated, high-throughput image analysis pipeline enables genetic studies of shoot and root morphology in carrot (Daucus carota L.)

2018 ◽  
Author(s):  
Sarah D. Turner ◽  
Shelby L. Ellison ◽  
Douglas A. Senalik ◽  
Philipp W. Simon ◽  
Edgar P. Spalding ◽  
...  
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Root Morphology ◽  
Agronomic Traits ◽  
Automated Analysis ◽  
Root Characteristics ◽  
Analysis Pipeline ◽  
Genetic Studies ◽  
Daucus Carota L ◽  
Size And Shape

AbstractCarrot is a globally important crop, yet efficient and accurate methods for quantifying its most important agronomic traits are lacking. To address this problem, we developed an automated analysis platform that extracts components of size and shape for carrot shoots and roots, which are necessary to advance carrot breeding and genetics. This method reliably measured variation in shoot size and shape, leaf number, petiole length, and petiole width as evidenced by high correlations with hundreds of manual measurements. Similarly, root length and biomass were accurately measured from the images. This platform quantified shoot and root shapes in terms of principal components, which do not have traditional, manually-measurable equivalents. We applied the pipeline in a study of a six-parent diallel population and an F2 mapping population consisting of 316 individuals. We found high levels of repeatability within a growing environment, with low to moderate repeatability across environments. We also observed co-localization of quantitative trait loci for shoot and root characteristics on chromosomes 1, 2, and 7, suggesting these traits are controlled by genetic linkage and/or pleiotropy. By increasing the number of individuals and phenotypes that can be reliably quantified, the development of a high-throughput image analysis pipeline to measure carrot shoot and root morphology will expand the scope and scale of breeding and genetic studies.

scholarly journals An Automated Image Analysis Pipeline Enables Genetic Studies of Shoot and Root Morphology in Carrot (Daucus carota L.)

2018 ◽  
Vol 9 ◽  
Author(s):  
Sarah D. Turner ◽  
Shelby L. Ellison ◽  
Douglas A. Senalik ◽  
Philipp W. Simon ◽  
Edgar P. Spalding ◽  
...  
Keyword(s):  
Image Analysis ◽  
Daucus Carota ◽  
Root Morphology ◽  
Automated Image Analysis ◽  
Analysis Pipeline ◽  
Genetic Studies ◽  
Daucus Carota L

scholarly journals A simple, cost-effective high-throughput image analysis pipeline improves genomic prediction accuracy for days to maturity in wheat

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Morteza Shabannejad ◽  
Mohammad-Reza Bihamta ◽  
Eslam Majidi-Hervan ◽  
Hadi Alipour ◽  
Asa Ebrahimi
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Cost Effective ◽  
Analysis Pipeline ◽  
Terminal Drought ◽  
Days To Maturity ◽  
Stress Ratios ◽  
Terminal Drought Stress

Abstract Background High-throughput phenotyping and genomic selection accelerate genetic gain in breeding programs by advances in phenotyping and genotyping methods. This study developed a simple, cost-effective high-throughput image analysis pipeline to quantify digital images taken in a panel of 286 Iran bread wheat accessions under terminal drought stress and well-watered conditions. The color proportion of green to yellow (tolerance ratio) and the color proportion of yellow to green (stress ratio) was assessed for each canopy using the pipeline. The estimated tolerance and stress ratios were used as covariates in the genomic prediction models to evaluate the effect of change in canopy color on the improvement of the genomic prediction accuracy of different agronomic traits in wheat. Results The reliability of the high-throughput image analysis pipeline was proved by three to four times of improvement in the accuracy of genomic predictions for days to maturity with the use of tolerance and stress ratios as covariates in the univariate genomic selection models. The higher prediction accuracies were attained for days to maturity when both tolerance and stress ratios were used as fixed effects in the univariate models. The results of this study indicated that the Bayesian ridge regression and ridge regression-best linear unbiased prediction methods were superior to other genomic prediction methods which were used in this study under terminal drought stress and well-watered conditions, respectively. Conclusions This study provided a robust, quick, and cost-effective machine learning-enabled image-phenotyping pipeline to improve the genomic prediction accuracy for days to maturity in wheat. The results encouraged the integration of phenomics and genomics in breeding programs.

scholarly journals HTPheno: An image analysis pipeline for high-throughput plant phenotyping

2011 ◽  
Vol 12 (1) ◽  
pp. 148 ◽  
Author(s):  
Anja Hartmann ◽  
Tobias Czauderna ◽  
Roberto Hoffmann ◽  
Nils Stein ◽  
Falk Schreiber
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Plant Phenotyping ◽  
Analysis Pipeline

An image analysis pipeline for the semi-automated analysis of clinical fMRI images based on freely available software

2010 ◽  
Vol 40 (3) ◽  
pp. 279-287 ◽  
Author(s):  
Christof Karmonik ◽  
Michele York ◽  
Robert Grossman ◽  
Ekta Kakkar ◽  
Krutina Patel ◽  
...  
Keyword(s):  
Image Analysis ◽  
Automated Analysis ◽  
Analysis Pipeline ◽  
Clinical Fmri

Morphology and fractal dimension of root systems of maize hybrids bearing the leafy trait

2003 ◽  
Vol 81 (7) ◽  
pp. 706-713 ◽  
Author(s):  
Carlos Costa ◽  
Lianne M Dwyer ◽  
Pierre Dutilleul ◽  
Kayhan Foroutan-pour ◽  
Aiguo Liu ◽  
...  
Keyword(s):  
Image Analysis ◽  
Fractal Dimension ◽  
Root Morphology ◽  
Growth Models ◽  
Morphological Characteristics ◽  
Root Systems ◽  
Root Characteristics ◽  
Root Branching ◽  
Randomized Design ◽  
Normal Stature

A rapid and accurate assessment of root morphological characteristics can be important for identifying promising plant genotypes. Scanner-based image analysis has made the measurement of root morphology faster, more accurate, and less subjective, and allows for estimation of fractal dimension (FD). We used scanner-based image analysis to investigate root morphology in leafy vs. conventional maize (Zea mays) genotypes. Two experiments were conducted following a completely randomized design with ten and five replicates. In the first experiment, treatments were factorial combinations of four hybrids (leafy reduced-stature (LRS), non leafy-reduced stature (NLRS), leafy normal stature (LNS), and non leafy normal stature (conventional commercial) hybrid, Pioneer 3979 (P3979)), and four sampling dates (5, 10, 15, and 20 days from transfer to growth pouches). In the second experiment, plants were grown for 15 days from emergence in 10-L plastic pots. This experiment included three maize genotypes (LRS, LNS, and Pioneer 3905 (P3905)). The FD of young root systems was similar for leafy genotypes (FD [Formula: see text] 1.66) and was higher for P3905 (FD [Formula: see text] 1.74). The FD correlated with root characteristics generally included in root growth models (0.68 [Formula: see text] r [Formula: see text] 0.87). Thus, FD could be used to summarize a number of root morphology variables.Key words: root morphology, fractal dimension, root branching, image analysis, maize.

scholarly journals Development of a semi-automated segmentation tool for high frequency ultrasound image analysis of mouse echocardiograms

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kristi Powers ◽  
Raymond Chang ◽  
Justin Torello ◽  
Rhonda Silva ◽  
Yannick Cadoret ◽  
...  
Keyword(s):  
Image Analysis ◽  
Automated Analysis ◽  
Structure And Function ◽  
Ultrasound Images ◽  
Cardiac Structure ◽  
Short Axis ◽  
Pixel Intensity ◽  
Cardiac Structure And Function ◽  
And Function

AbstractEchocardiography is a widely used and clinically translatable imaging modality for the evaluation of cardiac structure and function in preclinical drug discovery and development. Echocardiograms are among the first in vivo diagnostic tools utilized to evaluate the heart due to its relatively low cost, high throughput acquisition, and non-invasive nature; however lengthy manual image analysis, intra- and inter-operator variability, and subjective image analysis presents a challenge for reproducible data generation in preclinical research. To combat the image-processing bottleneck and address both variability and reproducibly challenges, we developed a semi-automated analysis algorithm workflow to analyze long- and short-axis murine left ventricle (LV) ultrasound images. The long-axis B-mode algorithm executes a script protocol that is trained using a reference library of 322 manually segmented LV ultrasound images. The short-axis script was engineered to analyze M-mode ultrasound images in a semi-automated fashion using a pixel intensity evaluation approach, allowing analysts to place two seed-points to triangulate the local maxima of LV wall boundary annotations. Blinded operator evaluation of the semi-automated analysis tool was performed and compared to the current manual segmentation methodology for testing inter- and intra-operator reproducibility at baseline and after a pharmacologic challenge. Comparisons between manual and semi-automatic derivation of LV ejection fraction resulted in a relative difference of 1% for long-axis (B-mode) images and 2.7% for short-axis (M-mode) images. Our semi-automatic workflow approach reduces image analysis time and subjective bias, as well as decreases inter- and intra-operator variability, thereby enhancing throughput and improving data quality for pre-clinical in vivo studies that incorporate cardiac structure and function endpoints.

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

scholarly journals Development of an Image Analysis Pipeline to Estimate Sphagnum Colony Density in the Field

Plants ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 840
Author(s):  
Willem Q. M. van de Koot ◽  
Larissa J. J. van Vliet ◽  
Weilun Chen ◽  
John H. Doonan ◽  
Candida Nibau
Keyword(s):  
Image Analysis ◽  
Organic Matter ◽  
Plant Density ◽  
Important Determinant ◽  
Carbon Sink ◽  
High Accuracy ◽  
Analysis Pipeline ◽  
Colony Density ◽  
Water Table Management ◽  
And Performance

Sphagnum peatmosses play an important part in water table management of many peatland ecosystems. Keeping the ecosystem saturated, they slow the breakdown of organic matter and release of greenhouse gases, facilitating peatland’s function as a carbon sink rather than a carbon source. Although peatland monitoring and restoration programs have increased recently, there are few tools to quantify traits that Sphagnum species display in their ecosystems. Colony density is often described as an important determinant in the establishment and performance in Sphagnum but detailed evidence for this is limited. In this study, we describe an image analysis pipeline that accurately annotates Sphagnum capitula and estimates plant density using open access computer vision packages. The pipeline was validated using images of different Sphagnum species growing in different habitats, taken on different days and with different smartphones. The developed pipeline achieves high accuracy scores, and we demonstrate its utility by estimating colony densities in the field and detecting intra and inter-specific colony densities and their relationship with habitat. This tool will enable ecologists and conservationists to rapidly acquire accurate estimates of Sphagnum density in the field without the need of specialised equipment.

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